generación distintos tipos de ruido

if (!require("pacman")) install.packages("pacman")
library(pacman)
# p_load(imager, seewave, tidyverse, knitr, patchwork)
p_load(imager, wavethresh, ggplot2, dplyr)

image_1_path <- file.path("fotos", "1.JPG")
image_1_original_cimg <- load.image(image_1_path)
image_1_fixed_cimg <- drop(image_1_original_cimg)

set.seed(123)
noise_std_dev <- 0.5
image_1_array <- as.array(image_1_fixed_cimg)

channels_original <- lapply(1:3, function(i) as.vector(image_1_array[, , i]))
noise <- lapply(channels_original, function(channel) rnorm(length(channel), mean = 0, sd = noise_std_dev))
channels_noisy <- Map(function(channel, n) pmax(0, pmin(1, channel + n)), channels_original, noise)

image_1_noisy_cimg <- as.cimg(
  array(
    unlist(channels_noisy),
    dim = c(dim(image_1_fixed_cimg)[1:2], 1, 3)
  )
)

par(mfrow = c(1, 2))
plot(as.cimg(image_1_original_cimg), main = "Original")
plot(image_1_noisy_cimg, main = "Noisy")

par(mfrow = c(1, 1))

library(imager)

# Load and prepare the image
image_2_path <- file.path("fotos", "2.JPG")
image_2_original_cimg <- load.image(image_2_path)
image_2_fixed_cimg <- drop(image_2_original_cimg)

# Convert image to array for manipulation
image_2_array <- as.array(image_2_fixed_cimg)

# Set noise parameters
frequency <- 50  # Frequency of sinusoidal noise
amplitude <- 0.2  # Amplitude of noise

# Generate sinusoidal noise for each channel
height <- dim(image_2_array)[1]
width <- dim(image_2_array)[2]

channels_original <- lapply(1:3, function(i) as.vector(image_2_array[, , i]))
noise <- lapply(channels_original, function(channel) {
  x <- seq(0, 2 * pi, length.out = width)
  y <- seq(0, 2 * pi, length.out = height)
  noise_grid <- outer(sin(x * frequency), sin(y * frequency))
  noise_vector <- as.vector(noise_grid) * amplitude
  noise_vector
})

# Add noise to each channel and clip values to [0, 1]
channels_noisy <- Map(function(channel, n) pmax(0, pmin(1, channel + n)), channels_original, noise)

# Reconstruct the noisy image
image_2_noisy_cimg <- as.cimg(
  array(
    unlist(channels_noisy),
    dim = c(dim(image_1_fixed_cimg)[1:2], 1, 3)
  )
)

# Plot the results
par(mfrow = c(1, 2))
plot(as.cimg(image_2_original_cimg), main = "Original")
plot(image_2_noisy_cimg, main = "Sinusoidal High-Frequency Noise")

par(mfrow = c(1, 1))